Cluster analysis of complex data is an essential task in statistics and machine learning. One of the most popular approaches in cluster analysis is the one based on mixture models. It includes mixture-model based clustering to partition individuals or possibly variables into groups, block mixture-model based clustering to simultaneously associate individuals and variables to clusters, that is c...

Prior distributions play a crucial role in Bayesian approaches to clustering. Two commonly-used prior distributions are the Dirichlet and Pitman-Yor processes. In this paper, we investigate the predictive probabilities that underlie these processes, and the implicit “rich-get-richer” characteristic of the resulting partitions. We explore an alternative prior for nonparametric Bayesian clusterin...

A Bayesian kernel-based clustering method is presented. The associated model arises as an embedding of the Potts density for label membership probabilities into an extended Bayesian model for joint data and label membership probabilities. The method may be seen as a principled extension of the so-called super-paramagnetic clustering. The model depends on three parameters: the temperature, the k...

Patent data have diversely technological information of any technology field. So, many companies have managed the patent data to build their R&D policy. Patent analysis is an approach to the patent management. Also, patent analysis is an important tool for technology forecasting. Patent clustering is one of the works for patent analysis. In this paper, we propose an efficient clustering method ...

We propose a Bayesian nonparametric regression model that exploits clustering for increased sensitivity in multiple hypothesis testing. We build on the recently proposed BEMMA (Bayesian Effects Models for Microarrays) method which is able to model dependence among objects through clustering and then estimates hypothesis-testing parameters averaged over clustering uncertainty. We propose several...

Phylogeographical analyses have become commonplace for a myriad of organisms with the advent of cheap DNA sequencing technologies. Bayesian model-based clustering is a powerful tool for detecting important patterns in such data and can be used to decipher even quite subtle signals of systematic differences in molecular variation. Here, we introduce two upgrades to the Bayesian Analysis of Popul...